Indexicon: A Spatial Indexing Library

Abstract

Spatial indexing is foundational to Geographic Information Systems (GIS) and multi-dimensional data management, yet the current open-source landscape poses a significant barrier to research that employs or benchmarks spatial access methods. We observe that most of the existing open-source libraries include a single index. Some are hindered by complex dependencies, missing critical functionalities, inconsistent APIs, and rigid constraints regarding the support of spatial data types. To address this issue, we introduce Indexicon: a unified, highly portable, extendable, open-source spatial indexing library, designed specifically for rapid integration and ease of modification of main-memory spatial access methods. Indexicon provides a comprehensive suite of popular tree-based spatial access methods, including the R-tree, Quad-tree variants, and the KD-tree. Each structure is meticulously implemented as a self-contained, single-file, header-only C++ template with zero external dependencies beyond the standard library. Crucially, every index features a uniform interface, natively supporting bulk-loading, dynamic insertions/deletions, range queries, $k$-nearest neighbor ($k$NN) search, and structural statistics tracking. We also present an extensive performance evaluation of Indexicon against well-established and widely used implementations of these structures (including Boost Geometry, PCL, and Nanoflann) across six real-world geographic datasets. Our results demonstrate that Indexicon's lightweight design matches or outperforms existing state-of-the-art implementations while offering unmatched architectural flexibility. To foster reproducible spatial research, we open-source the complete library alongside our datasets and query workloads.